8 Must-have Analytics Capabilities for a B2B Firm

In 2014-2015, predictive analytics was THE buzzword in the B2B world. The promise of marketing automation tools integrated with sales CRM had the industry enthused with how predictive could transform the marketing and sales functions.

In 2017, the focus has visibly shifted to machine learning and artificial intelligence, and there is a similar excitement of how these technologies can transform businesses.

However, if one strips out the technology and tools from the business application or capability, the fundamental needs remain unaddressed to a large extent. So what are the analytics capabilities a mid-sized B2B firm must have?

BRIDGEi2i has identified eight capabilities that are essential for a mid-sized B2B firm to sell to its customers efficiently. The two axes we used for prioritization are ‘Ease of Implementation’ and ‘Expected ROI’.

1. Market and Sales Planning

With growth being a focus for most organizations, the market/sales planning activity should be of critical importance. Most mid-sized firms, however, do not have the capability to do this with the right rigor. Hence, the plans end up reflecting the confidence or capability of their sales team rather than the true opportunity that exists.

Market sizing at a geo, vertical, and account level; share of wallet in existing accounts; account segmentation; GTM planning; and coverage models are essential to a solid planning process. Companies looking to build analytics capabilities prioritize this area way on top.

2. Pipeline Forecasting / Opportunity Scoring

Pipeline predictability, or the lack of it, is one of the most common problems senior executives face. Pipeline forecasting/sufficiency analysis and opportunity scoring is, therefore, a capability that most B2B firms get quick ROI from.

Most firms have made huge investments in sales CRM engines, and there have been mixed results in driving the adoption of these engines. Nonetheless, these firms have the data and environment ready for pipeline forecasting and opportunity scoring.

3. Installed Base Mining

Although this may sound obvious, installed base mining capability in terms of segmentation, best customer modeling, propensity modeling for cross-sell, and renewal/churn modeling are capabilities that most B2B firms are still looking to build. Thanks to millions spent on MDM, data quality is at an acceptable level for building these capabilities.

4. Channel Intelligence

Channel coverage and strength is one of the critical priorities, and using analytics to segment your own/competitor’s channel, design channel incentives, optimize channel development, and monitor performance is a critical capability.

5. Lead Intelligence

B2B has been long measured by the contribution to the sales funnel. Without getting into the merits of whether this is right or not, the capability to analyze MQL to SQL conversion, discriminate the sources for good vs. bad leads, evaluate new lead scores, and improve lead scoring has never been more important. Most firms that invested in marketing automation tools are finding that this isn’t a problem technology can resolve fully. To do this right, a matured analytics capability is required.

6. Pricing

Growth and margin are often seen as contrary goals. Although there is truth to it, leaving money on the table or losing deals because of pricing constitute a significant risk and opportunity for most firms. A small but agile pricing bid desk looking at price waterfalls, distribution pricing, contractual pricing, and optimizing win-loss ratios again is gaining ground. The use of machine learning and quote bots is only going to increase in the space.

7. Sales Force Effectiveness

This is of obvious interest as this is an area that hurts most when not functioning well. Measuring sales force productivity is complex and politically charged, but there are certain objective ways of going about this. Even the first step of creating the right metrics, such as quota-to-pipeline ratio, opportunity conversion ratios, peer group comparisons, and % time in customer-facing activities, is the right step in the direction. The focus needs to be on removing the bottlenecks to productivity instead of measuring it most accurately as it can be an exercise in futility.

8. Digital Analytics

With most marketing dollars getting into the digital kitty and organizations striving to provide superior customer experience, digital analytics is fundamental for digital transformation. Therefore, this isn’t a single capability but an umbrella name for many capabilities.

However, digital content analytics is an area we call out as an area of strategic importance, and we have seen a lot of interest in analytics around content. It’s true that there are only two C’s in B2B marketing — the customer and the content.

Large B2B companies with large data science organizations have built these well because they could afford the investments in the space. But how do mid-sized companies build these capabilities?

The first reaction is for the organization to hire a two or three-member data science team to build the capabilities, but the realization soon comes that there is a critical mass of data science team required to execute these projects without which the investments never materialize. Also, the most important part of an analytics success story is not the capability itself but the change management needed to implement it.

However, there are ways to build skeletal forms of these capabilities in less than a year and then improve over time. The good thing about these capabilities is that once stood up, they take a life of their own and provide lasting ROI and transform business processes.

Thank you for reading this blog. I would love to hear your views. Please share how you would prioritize these capabilities, and mention others that you think are important.

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